A novel method for salient object detection via compactness measurement

Salient object detection is a process of extracting an object which is visually attractive from a single image or a video. As a powerful technique for automatic image or video segmentation, saliency detection has been focused and studied recently. In this paper, we propose a novel method for salient object detection without training or learning-based techniques. The proposed framework consists of two major steps, the generation of saliency map candidates and the selection of an optimal saliency map. To generate saliency map candidates, prior maps based on combinations of RGB color components are proposed. To select the optimal saliency map among the candidates, we propose a compactness measure, which evaluates the degree to which the generated saliency maps show objects. As a result, among recent works on saliency detection, our saliency detection method achieves the highest performance in terms of saliency detection.